Uses of Class
com.rapidminer.operator.AbstractExampleSetProcessing

Packages that use AbstractExampleSetProcessing

com.rapidminer.operator.features	Provides feature handling operators.
com.rapidminer.operator.features.construction	Provides operators for automatic feature construction.
com.rapidminer.operator.features.selection	Provides operators for automatic feature selection.
com.rapidminer.operator.features.transformation	Provides operators for feature space transformations like PCA or ICA.
com.rapidminer.operator.postprocessing	Operators for post processing, usually used for models.
com.rapidminer.operator.preprocessing	Operators for preprocessing purposes.
com.rapidminer.operator.preprocessing.filter	Containing filter operators changing the input example set, e.g. by removing certain attributes or changing the data.
com.rapidminer.operator.preprocessing.filter.attributes	This package contains the attribute filter.
com.rapidminer.operator.preprocessing.outlier	Operators for outlier detection.
com.rapidminer.operator.preprocessing.sampling	Preprocessing operators used for sampling.
com.rapidminer.operator.preprocessing.series	Containing preprocessing operators for (time) series handling.
com.rapidminer.operator.preprocessing.series.filter	Containing preprocessing operators for (time) series filtering.
com.rapidminer.operator.preprocessing.transformation	This package contains some simple operators for basic transformations like grouping, aggregation and pivotization.
com.rapidminer.operator.preprocessing.weighting	This package methods for the weighting of examples.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.features

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.features

class

AbstractFeatureProcessing Superclass of all operators changing the features (attributes) of an ExampleSet.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.features.construction

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.features.construction

class	AbstractFeatureConstruction Abstract superclass of all feature processing operators who generate new features.
class	AttributeAggregationOperator Allows to generate a new attribute which consists of a function of several other attributes.
class	AttributeConstruction This operator constructs new attributes from the attributes of the input example set.
class	CompleteFeatureGenerationOperator This operator applies a set of functions on all features of the input example set.
class	ConditionedFeatureGeneration Generates a new attribute and sets the attributes values according to the fulfilling of the specified conditions.
class	FeatureGenerationOperator This operator generates new user specified features.
class	GaussFeatureConstructionOperator Creates a gaussian function based on a given attribute and a specified mean and standard deviation sigma.
class	LinearCombinationOperator This operator applies a linear combination for each vector of the input ExampleSet, i.e.
class	ProductGenerationOperator This operator creates all products of the specified attributes.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.features.selection

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.features.selection

class	AbstractFeatureSelection Abstract superclass of all feature processing operators who remove features from the example set.
class	RandomSelection This operator selects a randomly chosen number of features randomly from the input example set.
class	RemoveCorrelatedFeatures Removes (un-) correlated features due to the selected filter relation.
class	RemoveUselessFeatures Removes useless attribute from the example set.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.features.transformation

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.features.transformation

class	AbstractFeatureTransformation Abstract super class of all operators transforming the feature space.
class	FourierTransform Creates a new example set consisting of the result of a fourier transformation for each attribute of the input example set.
class	PrincipalComponentsTransformation Builds the principal components of the given data.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.postprocessing

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.postprocessing

class	SimpleUncertainPredictionsTransformation This operator sets all predictions which do not have a higher confidence than the specified one to "unknown" (missing value).
class	WindowExamples2OriginalData This operator performs several transformations which could be performed by basic RapidMiner operators but lead to complex operator chains.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing

class	AbstractDataProcessing Abstract super class of the AbstractExampleSetProcessing hierarchy in the preprocessing package.
class	Deobfuscator This operator takes an ExampleSet as input and maps all nominal values to randomly created strings.
class	ExampleSetTranspose This operator transposes an example set, i.e. the columns with become the new rows and the old rows will become the columns.
class	GuessValueTypes This operator can be used to (re-)guess the value types of all attributes.
class	IdTagging This operator adds an ID attribute to the given example set.
class	MaterializeDataInMemory Creates a fresh and clean copy of the data in memory.
class	NoiseOperator This operator adds random attributes and white noise to the data.
class	Obfuscator This operator takes an ExampleSet as input and maps all nominal values to randomly created strings.
class	UseRowAsAttributeNames This operators uses the values of the specified row of the data set as new attribute names (including both regular and special columns).

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.filter

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.filter

class	AbsoluteValueFilter This operator simply replaces all values by their absolute respective value.
class	AddNominalValue Adds a value to a nominal attribute definition.
class	AttributeAdd This operator creates a new attribute for the data set.
class	AttributeCopy Adds a copy of a single attribute to the given example set.
class	AttributeMerge This operator merges two attributes by simply concatenating the values and store those new values in a new attribute which will be nominal.
class	AttributeValueMapper This operator takes an ExampleSet as input and maps the values of certain attributes to other values.
class	AttributeValueReplace This operator creates new attributes from nominal attributes where the new attributes contain the original values which replaced substrings.
class	AttributeValueSplit This operator creates new attributes from a nominal attribute by dividing the nominal values into parts according to a split criterion (regular expression).
class	AttributeValueSubstring This operator creates new attributes from nominal attributes where the new attributes contain only substrings of the original values.
class	AttributeValueTrim This operator creates new attributes from nominal attributes where the new attributes contain the trimmed original values, i.e. leading and trailing spaces will be removed.
class	ChangeAttributeName This operator can be used to rename an attribute of the input example set.
class	ChangeAttributeNames2Generic This operator replaces the attribute names of the input example set by generic names like att1, att2, att3 etc.
class	ChangeAttributeNamesReplace This operator replaces parts of the attribute names (like whitespaces, parentheses, or other unwanted characters) by a specified replacement.
class	ChangeAttributeRole This operator can be used to change the attribute type of an attribute of the input example set.
class	ChangeAttributeType This operator can be used to change the attribute type of an attribute of the input example set.
class	Construction2Names This operator replaces the names of the regular attributes by the corresponding construction descriptions if the attribute was constructed at all.
class	Date2Nominal This operator transforms the specified date attribute and writes a new nominal attribute in a user specified format.
class	Date2Numerical This operator changes a date attribute into a numerical one.
class	DateAdjust
class	ExampleFilter This operator takes an ExampleSet as input and returns a new ExampleSet including only the Examples that fulfill a condition.
class	ExampleRangeFilter This operator keeps only the examples of a given range (including the borders).
class	ExchangeAttributeRoles This operator changes the attribute roles of two input attributes.
class	FeatureBlockTypeFilter This operator switches off all features whose block type matches the one given in the parameter skip_features_of_type.
class	FeatureFilter This is an abstract superclass for feature filters.
class	FeatureNameFilter This operator switches off all features whose name matches the one given in the parameter skip_features_with_name.
class	FeatureRangeRemoval This operator removes the attributes of a given range.
class	FeatureValueTypeFilter This operator switches off all features whose value type matches the one given in the parameter skip_features_of_type.
class	InfiniteValueReplenishment Replaces positive and negative infinite values in examples by one of the functions "none", "zero", "max_byte", "max_int", "max_double", and "missing".
class	InternalBinominalRemapping Correct internal mapping of binominal attributes according to the specified positive and negative values.
class	MergeNominalValues Merges two nominal values of a given regular attribute.
class	MissingValueReplenishment Replaces missing values in examples.
class	MissingValueReplenishmentView This operator simply creates a new view on the input data without changing the actual data or creating a new data table.
class	Nominal2Date This operator parses given nominal attributes in order to create date and / or time attributes.
class	Nominal2String Converts all nominal attributes to string attributes.
class	NominalNumbers2Numerical This operator transforms nominal attributes into numerical ones.
class	Numerical2Real Converts all numerical attributes (especially integer attributes) to real valued attributes.
class	NumericToBinominal Converts all numerical attributes to binary ones.
class	NumericToFormattedNominal This operator tries to parse numerical values and formats them in the specified number format.
class	NumericToNominal Converts all numerical attributes to nominal ones.
class	NumericToPolynominal Converts all numerical attributes to nominal ones.
class	PermutationOperator This operator creates a new, shuffled ExampleSet by making a new copy of the exampletable in main memory!
class	Real2Integer Converts all real valued attributes to integer valued attributes.
class	RemoveDuplicates This operator removed duplicates from an example set by comparing all examples with each other on basis of the specified attributes.
class	SetData This operator simply sets the value for the specified example and attribute to the given value.
class	Sorting This operator sorts the given example set according to a single attribute.
class	String2Nominal Converts all string attributes to nominal attributes.
class	TFIDFFilter This operator generates TF-IDF values from the input data.
class	ValueReplenishment Abstract superclass for all operators that replenish values, e.g. nan or infinite values.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.filter.attributes

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.filter.attributes

class

AttributeFilter This operator filters the attributes of an exampleSet.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.outlier

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.outlier

class	AbstractOutlierDetection Abstract superclass of outlier detection operators.
class	DBOutlierOperator This operator is a DB outlier detection algorithm which calculates the DB(p,D)-outliers for an ExampleSet passed to the operator.
class	DKNOutlierOperator This operator performs a D^k_n Outlier Search according to the outlier detection approach recommended by Ramaswamy, Rastogi and Shim in "Efficient Algorithms for Mining Outliers from Large Data Sets".
class	LOFOutlierOperator This operator performs a LOF outlier search.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.sampling

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.sampling

class	AbsoluteSampling Absolute sampling operator.
class	AbsoluteStratifiedSampling Stratified sampling operator.
class	AbstractBootstrapping This operator constructs a bootstrapped sample from the given example set.
class	AbstractSamplingOperator Abstract superclass of operators leaving the attribute set and data unchanged but reducing the number of examples.
class	AbstractStratifiedSampling Abstract superclass of stratified sampling operators.
class	Bootstrapping This operator constructs a bootstrapped sample from the given example set.
class	KennardStoneSampling This operator performs a Kennard-Stone Sampling.
class	ModelBasedSampling Sampling based on a learned model.
class	RatioStratifiedSampling Stratified sampling operator.
class	SimpleSampling Simple sampling operator.
class	WeightedBootstrapping This operator constructs a bootstrapped sample from the given example set which must provide a weight attribute.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.series

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.series

class	AbstractSeriesProcessing This is the abstract superclass for all series processing operators.
class	EnsureMonotonicity This operator filters out all examples which would lead to a non-monotonic behaviour of the specified attribute.
class	FillDataGaps This operator fills gaps in the data based on the ID attribute of the data set.
class	LabelTrend2Classification This operator iterates over an example set with numeric label and converts the label values to either the class 'up' or the class 'down' based on whether the change from the previous label is positive or negative.
class	MultivariateSeries2WindowExamples This operator transforms a given example set containing series data into a new example set containing single valued examples.
class	Series2WindowExamples This is the superclass for all series to example transformation operators based on windowing.
class	SingleAttributes2ValueSeries Transforms all regular attributes of a given example set into a value series.
class	UnivariateSeries2WindowExamples This operator transforms a given example set containing series data into a new example set containing single valued examples.
class	WindowExamples2ModelingData This operator performs several transformations related to time series predictions based on a windowing approach.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.series.filter

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.series.filter

class	CumulateSeries Generates a cumulative series from another series.
class	DifferentiateSeries This operator extracts changes from a numerical time series by comparing actual series values with past (lagged) values.
class	ExponentialSmoothing Creates a new series attribute which contains the original series exponentially smoothed.
class	IndexSeries Creates an index series from an original series.
class	MovingAverage Creates a new series attribute which contains the moving average of a series.
class	SeriesMissingValueReplenishment Replaces missing values in time series.

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.transformation

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.transformation

class

AggregationOperator This operator creates a new example set from the input example set showing the results of arbitrary aggregation functions (as SUM, COUNT etc. known from SQL).

Uses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.weighting

Subclasses of AbstractExampleSetProcessing in com.rapidminer.operator.preprocessing.weighting

class

EqualLabelWeighting This operator distributes example weights so that all example weights of labels sum up equally.